Today's e-commerce applications do not provide the service expected by customers or the performance demanded by e-merchants. In brick-and-mortar establishments, attentive sales staff members assume the role of assistant and guide, enabling business transactions that optimize customer satisfaction and transaction value. In person-to-person interactions, merchants are able to respond quickly and flexibly to customer concerns and to provide information to the customer as appropriate. Simultaneously, the face-to-face interactions provide opportunities to sell customers the products that satisfy their requirements and Maximize profit for the merchant. Current e-commerce implementations are neither, dynamic nor responsive in the time frames necessary to assess a customer's needs and provide the products and terms that will close the sale.
In e-commerce, visitors to e-commerce web sites are always potential customers. The analysis of web visitor behavior is currently accomplished through the use of web site analytics and integrative systems that allow consolidation and access to large volumes of historical data. While these tools provide useful retrospective insights on customers and products, they cannot model web visitor behavior accurately in real-time. Therefore, the responsive relationship between a salesperson and a customer in a brick-and-mortar store, with all its demonstration, information and upsell opportunities, simply does not exist in e-commerce. Standard statistical approaches are a poor fit with online behavioral data. Effective real-time web visitor behavior modeling requires making increasingly customer-centric, accurate inferences, often with extremely sparse data, associated with individual web visitors over very short time periods.
True customer-centric interaction can only take place when the web site responds to each web visitor in an appropriate and personalized manner. Previous attempts at understanding customer intent and needs in real-time have failed, largely because the simple modeling paradigms employed were incapable of integrating the rich detail available about a web visit. For example, it is standard in web-visitor behavior modeling to pre-classify web pages into a handful of distinct types, and try to identify path profiles from transitions over page types. In fact, there are several classes of information that can be exploited in understanding customer behavior. These classes are Visitor demographics, if the visitor is known to the site (e.g., zip code, time since last visit); Session-level visitor information (e.g., time of day, etc); Page location information (e.g., with web-site overall structure); Page presentation information (e.g., ad content, side-bar information presented, etc); Content information (e.g., attributes of specific products presented in detail, if any); and Click-stream dynamics, including both inter and intra-click dynamic information (e.g., inter-click elapsed time, mouse-overs).
Integration of all this information into a comprehensive model of the real-time behavior of a web site visitor is difficult or impossible without a modeling approach that can account for the complex relationships among these various types of data. This is especially true when some of the parameters may be unobservable (e.g., visitor intent). Relational Bayesian modeling combines a relational data model with the probabilistic semantics needed to effectively model the stochastic elements of observable behavior. One challenge in understanding dependencies among relational data is one-to-many relationships among data elements.
A prior art paper by Getoor et al [Getoor et al, 1999] have suggested the use of aggregation operators (cardinality, max, min, mean, sum, etc. . . . ) to model dependence from many to one. For example, at the relational level one might model that session purpose is closely correlated with the number of page requests in a session (i.e., cardinality). Little work has been done on identifying a broadly applicable set of such operators, nor has the adequacy of this approach been evaluated. A complementary alternative is the notion of selection, as used in Inductive Logic Programming. For example, one might surmise that what is most informative isn't simply the number of page requests in a session, but rather the number of page requests where PageRequest.page.type=catalog_page. Others have suggested combining rules [Laskey, 2001], and default mechanisms for combining information from multiple, similar, sources.
As an example, consider the following simple dynamic model of web-site visitors. FIG. 1 is the schema for a simple relational database of web server page requests. FIG. 2 is a simple relational Bayesian model over this database.
FIG. 3 shows the instance-level map for a visitor with only one session, consisting of two page requests. Note that the part of the schema-level model for page-requests has been replicated for each actual page request.
Another model contains the subgraph consisting of Session.purpose and the two PageRequest.page.type nodes, as shown in FIG. 4. Suppose that, at the schema level, the arc from Session.purpose to PageRequest.page.type had been reversed. Then, at the instance level, this would form the subgraph shown in FIG. 4. The semantics of Bayesian networks require that, for such a subgraph, P(Session.purpose 1 PageRequest1.page.type, PageRequest2.page.type) must be known. The relational model provides, however, a generic P(Session.purpose1PageRequest.page.type), These prior art techniques do not contain a practical way to extend this distribution to handle multiple conditioning variables. Therefore presently there is no effective way to model and predict user behavior in an e-commerce environment using prior art techniques.